AI、意识与心智的未来

我认为人工智能引发了大量极其有趣的哲学问题。比如，人类心智的本质是什么？心智的本质是什么？意识呢？我确实认为这是个错误的问题，而且从很多方面来看都是错的。你觉得人工智能的推理能力有多强？嗯，这是一个非常有趣且尚未定论的问题，甚至有些争议。想想看，每个今天出生的孩子，他们成长的世界里，机器从来都能和他们对话，这真的很惊人。欢迎回到 Google DeepMind 播客。本期的嘉宾是 Murray Shanahan，伦敦帝国理工学院认知机器人学教授，也是 Google DeepMind 的首席研究科学家。我们都听说过有人爱上聊天机器人、有人推动大语言模型思考自身存在、或者质疑它们对现实概念理解极限的故事。但这类关于自我认同、思考和元认知的问题，已经困扰了哲学家数千年。因此，他们转向 AI 来追问关于 AI 智能本质、当前能力、甚至意识与否的最深刻问题，也是合情合理的。Murray Shanahan 从上世纪 90 年代就开始从事 AI 研究。如果你一直关注本播客，你会记得他是 2014 年科幻电影《机械姬》的顾问，该片讲述了一位程序员有机会测试女性机器人 Ava 的智能，并最终质疑她是否有意识。欢迎回到播客，Murray。

I think there are just a huge number of enormously interesting philosophical questions that AI gives rise to. You know, what is the nature of the human mind? What is the nature of mind? What about consciousness? I do think that is the wrong question and I think it's wrong in many ways. How good do you think the AI is at reasoning? Well, that's a very interesting and kind of open question and somewhat controversial. You know, it really is astonishing to think that every single child born today, they're going to grow up in a world where they've never known a world in which machines can't talk to them. Welcome back to Google DeepMind the podcast. My guest on this episode is Murray Shanahan, professor of cognitive robotics at Imperial College London and principal research scientist at Google DeepMind. Now, we have all heard the stories about people falling in love with their chatbots, about people pushing large language models to contemplate their own existence or questioning the limits of their conceptual understanding of reality. But these kinds of questions about self-identity and thinking and metacognition have been puzzling philosophers for millennia already. And so it makes sense that they should be turning to AI to interrogate the most profound questions about the nature of AI's intelligence, of its current capabilities, even its consciousness or otherwise. Murray Shanahan has been working in the field of AI since the 1990s. And if you've been following this podcast for a while, you will remember him as the man that consulted on the 2014 science fiction film Ex Machina about a computer programmer who gets the chance to test the intelligence of a female robot Ava and ultimately questions whether she is conscious. Welcome back to the podcast, Murray.

回想一下，我知道你在《机械姬》——也就是亚历克斯·加兰的电影——中扮演了关键角色。你认为那部电影以及当时其他科幻电影中，哪些地方是对的？我是说回想 10 到 15 年前，我们走在正确的轨道上吗？

Just thinking back, because I know that you played a key role in Ex Machina, should we say, the Alex Garland film. What do you think you got right in that film and in other science fiction films that were around at the time? I mean thinking back to sort of 10, 15 years ago, were we on the right track?

《机械姬》做得非常好的一点是，它提出了大量关于意识、AI 与意识、以及意识本身的有趣且发人深省的问题。所以这是一个巨大的成功。但有趣的是，就在《机械姬》上映前不久，《她》上映了。就是斯派克·琼斯的电影《她》。当时我其实并不太喜欢《她》这部电影，因为我觉得一个人爱上这种没有实体的声音太不现实了，即使是斯嘉丽·约翰逊的声音。我错得有多离谱？作为预测，《她》在预测我们今天的世界方面做得非常好。我们不知道未来几年事情会如何发展，因为也许机器人技术也会像 AI 语言能力一样快速发展，但目前主要还是无实体的语言。而且，《她》展示了人们实际上可以在最广泛的意义上与无实体的 AI 系统建立关系，这真的很了不起。

So one respect in which Ex Machina really did a great service was that it does raise a whole load of very interesting and provocative questions about consciousness and about AI and consciousness, and therefore about consciousness itself. So that's one huge success. But it's interesting that just very shortly before Ex Machina came out, Her came out. So Spike Jonze's movie Her came out. At the time, I really wasn't all that keen on Her as a movie, because I just thought it was so implausible that a person could fall in love with this kind of disembodied voice, even if it's Scarlett Johansson. I mean, how wrong was that? As a bit of prediction, I think Her really did amazingly well at predicting the world we got now. Now we don't know quite how things are going to unfold in the next few years, because maybe robotics will progress rapidly as well in the way that language has in AI, but at the moment it's all about disembodied language. But also, Her showed how people can in fact be very much form relationships in the broadest sense with disembodied AI systems, which is an extraordinary thing really.

好吧，我们说的是 10 到 15 年前。但你在 AI 领域的参与要早得多。你认识约翰·麦卡锡。

Okay, we're talking 10-15 years ago. But your involvement in AI goes back much further than this. You knew John McCarthy.

我确实认识约翰·麦卡锡。我和他很熟。约翰·麦卡锡当年是计算机科学和人工智能教授。他实际上创造了“人工智能”这个词，并且是 1956 年著名的达特茅斯会议提案的作者之一，那是世界上第一次 AI 会议。那次会议真正勾勒出了整个领域。当时人们根本没有认真考虑这类事情，只有少数人。所以我认为他是一位真正的激进思想家，一直都是。

I did know John McCarthy. I knew him very well. John McCarthy was a professor of computer science and artificial intelligence back in the day. He actually coined the phrase 'artificial intelligence' and was one of the authors of the proposal for the very famous Dartmouth conference that took place in 1956, which was the first AI conference in the world. And that conference really mapped out the whole field. People just weren't thinking about this kind of thing seriously at all. It was just a handful. So I think he was a real radical thinker and always was.

好吧。1955 年选用的“人工智能”这个词，是个好选择吗？

Okay. That choice of words, 'artificial intelligence' back in 1955. Was it a good choice of words?

是的。我仍然认为这是个好选择。我知道有些人认为这不是个好选择，但让我说说他们的一些论点。首先，是“智能”这个词。智能本身在某种程度上是一个非常有争议的概念，尤其是当人们想到智商测试，以及智能可以在一个简单的尺度上量化，有些人比其他人更聪明这种想法。我认为在心理学中，今天已经公认存在多种不同的智能。这是一个非常重要的点。所以对这个词存在担忧。

Yeah. I mean, I still think it was. I know that some people don't think it was a good choice of words, but let me give you some of their arguments. First of all, there is the word 'intelligence'. Intelligence itself is in some ways a very contentious concept, especially if people think about IQ tests and the idea that intelligence is something that can be quantified on a straightforward simple scale, and then some people are more intelligent than others. And I think in psychology, it's well recognized today that there are many different kinds of intelligence. And this is a really important point. So there is that concern about that word.

那你会用什么不同的词？

So what would you have used differently?

嗯，也许“人工认知”之类的。我经常用“认知”这个词来表示思考和处理信息等。

Well, maybe 'artificial cognition' or something. I often use the word 'cognition' to mean thinking and processing information and so on.

听起来没那么顺耳，对吧？老实说，是的。尤其是现在。我觉得我们已经在这条路上走得太远了，不是吗？

It doesn't have the same ring to it, does it? Let's be honest. No. Especially not now. I think we're too far down this road, aren't we?

是的。“人工”这个词，我对“人工”这个词没什么意见。这似乎是合适的。它暗示了这是我们所建造的、并非自然演化的事物。所以这似乎是个合适的词。对这个词的反对意见，我想，是人工智能所构建的一切最终在某种程度上都是由人类构建的。

Yeah. The word 'artificial', I don't really have a problem with the word 'artificial'. That seems like the right kind of thing. It's alluding to the fact that it's something that we've built and that hasn't evolved in nature. And so that seems the right sort of word. The objection to that word, I guess, is that ultimately everything that artificial intelligence is built on is at some level constructed by humans.

当然。是的。但确实如此。那么在这种情况下这个词有什么问题呢？

Sure. Yes. But it is. So what's wrong with the word in that case?

我的意思是，我认为那是事实。

I mean, I think that's true.

你当时在研究符号 AI，对吧？跟我们说说它和其他类型的区别，以及我们现在在这方面的情况。

You were working on symbolic AI, right? Just talk to us about the difference between that and the other types and where we're at now with that.

当然。所谓的人工智能符号范式在几十年来一直非常突出、非常主导。其理念是，一切都关乎符号以及类似语言的句子和符号的操作，使用这些符号进行推理过程。经典例子是专家系统。在 20 世纪 80 年代，人们构建这些专家系统，想法是尝试将医学知识编码为一组规则，规则类似于：如果病人体温 104 度且皮肤发紫，那么他们有 0.75%的概率得了皮肤炎之类的。你看得出我不是医生。

Absolutely. So the so-called symbolic paradigm of artificial intelligence was very much preeminent, very much dominant for decades. So the idea there is that it's all about the manipulation of symbols and of language-like sentences and symbols, using kind of reasoning processes with those symbols. So the classic example would be an expert system. So back in the 1980s, people were building these expert systems, and the idea was that you would try to encode medical knowledge, say, in a set of rules, and the rules would be something like: if the patient has a temperature of 104 and their skin is purple, then there's a 0.75% probability that they've got skinnyitis or something. You can tell that I'm not a medical doctor.

然后你会把成千上万条这样的规则放进一个大型知识库，再有一个所谓的推理引擎，对这些规则进行逻辑推理，得出关于可能疾病的结论。但很大程度上就是一堆‘如果这样，那么那样’的规则。其中一个主要问题是：规则从哪来？得有人把它们全部写出来。于是就有了知识获取这个领域，你去拜访专家，试图提取他们在某个领域（比如医疗诊断、修复印机、法律）的理解，然后把这些全部编码成计算机能理解的、非常精确的规则。这个过程非常繁琐，而且最终得到的东西非常脆弱，会在各种情况下出错。另一个重要的研究领域是常识，因为人们意识到我们隐含地拥有大量关于日常世界的常识知识——物体是固体的、以特定方式运动、相互契合，还有液体、气体、重力等等。我们一直在无意识地运用这些知识。所以有一些大型项目试图把所有这些常识知识编码成公理、逻辑和规则，那简直是一场噩梦。所以到 21 世纪初，我真的认为这个研究范式注定要失败，我开始远离它。但后来神经网络之类的东西出现了，它不太涉及‘如果-那么’规则，而更多是从大量数据中提取信息。

And then so you'd have thousands and thousands of these sorts of rules would be put into a kind of big knowledge base and then you'd have what was called an inference engine which would carry out logical reasoning over all of these rules and come to some conclusion about what the likely disease was. But it was a lot of if this then that rules largely. And one of the big problems with that is where do the rules come from? Well, somebody has to write them all out. And so there was a whole field of knowledge elicitation where you go around to experts and try to extract their understanding in their domain, which could be medical diagnosis, fixing photocopiers, or the law, and you try to codify all of this into computer-comprehensible very precise rules. That was a very cumbersome process. And also what you ended up with was very brittle. It would go wrong in all kinds of ways. Another big area of research was common sense because often it was realized that we implicitly have an enormous amount of common sense knowledge about the everyday world—the fact that objects are solid, move in certain ways, fit into each other, liquids, gases, gravity, all kinds of things. And we bring all that knowledge to bear all the time, but it's unconscious. So there were big projects to try to codify all that common sense knowledge into axioms and logic and rules, which was a nightmare. So by the early 2000s, I really thought this research paradigm was doomed. I started moving away from it. But then along came things like neural networks, which was much less about if-then rules and much more about extracting information from large amounts of data.

是的。

Yes.

但我想知道，既然语言问题已经被有效攻克，我们是否达到了一个更高的抽象层次，可以回归一些符号技术？

But then I wonder now that language is effectively cracked, have we reached a higher level of abstraction where we can go back to some of those symbolic techniques?

我们确实可以，因为如今大语言模型的热门话题之一就是推理。所以有了所谓的思维链模型，它们不是简单地生成问题的答案，而是在给出答案之前生成一整套推理链条，这非常有效。有趣的是，这在很多方面呼应了符号 AI 时代人们的研究，但底层基础非常不同，因为不是硬编码的规则，而是学习过的神经网络。

Well, we certainly have, because nowadays one of the hot topics with large language models is reasoning. So you have these so-called chain of thought models that, rather than simply generating an answer to a question, they generate a whole chain of reasoning before issuing the answer, and that can be very effective. So it's interesting how that harks back in many ways to what people were looking at in the days of symbolic AI, but the underlying substrate is very different because it's not hardcoded rules; it's neural networks that have learned.

我想接着谈谈推理这一点。作为有逻辑学背景的哲学家，你认为 AI 的推理能力有多强？

Let me pick up on that point about reasoning. As a philosopher with a background in logic, how good do you think the AI is at reasoning?

这是一个非常有趣且有些争议的开放性问题。计算机科学家和 AI 研究者对推理有特定的概念，可以追溯到形式逻辑和定理证明。在符号 AI 时代，有些系统非常擅长用形式逻辑进行定理证明。所以人们认为那才是真正的推理。今天的大语言模型无法与已经存在了几十年的手写定理证明器或逻辑引擎的性能相媲美。

Well, that's a very interesting and somewhat controversial open question. Computer scientists and AI people have a particular notion of reasoning that harks back to formal logic and theorem proving. In the days of symbolic AI, you had systems that were very good at theorem proving with formal logic. So people think that's proper reasoning. Today's large language models can't match the performance of a handcoded theorem prover or logic engine that's been around for decades.

举个例子，什么样的定理可以由硬编码系统证明？

Give me an example of a type of theorem that might be proved by a hardcoded system.

可以是像‘1 后面的数字是 2’这样的东西，在数论或非常数学的领域。但也可以是更日常的事情，比如一个非常困难的物流规划问题，有数百辆卡车、仓库、货物，你需要规划路线和调度。这是一个计算上困难的问题，可以用形式规则非常精确地表达，你可能想用老式的规划算法。当代大语言模型在这方面越来越好，但你没有数学保证它们总能得出正确答案，而且很容易构造出更多公理的例子让它们出错。还有一个独立的研究方向，就是构建结合当今 AI 和老式符号技术的硬编码系统，专门用于数学定理证明，DeepMind 在这方面做了很棒的工作。但这与大语言模型不同。对于大语言模型，我们考虑的是可以谈论任何事情的聊天机器人，它们能做的事情之一是某种推理，但不如为特定任务手工构建的系统那么好。

It could be something like the number that follows one is two, in the domain of number theory or something very mathematical. But it could be something more everyday, like a very difficult logistical planning problem where you have hundreds of lorries, depots, goods, and you need to plan routes and deployment. That's a computationally difficult problem that can be expressed very precisely in formal rules, and you might want to use a good old-fashioned planning algorithm. Contemporary large language models are getting better at this kind of thing, but you don't have mathematical guarantees that they'll always come up with the right answer, and it's easy to make examples with more axioms where they slip up. There's a separate research direction to build handcoded things that combine today's AI with old-fashioned symbolic techniques for mathematical theorem proving, and DeepMind has done amazing work along those lines. But that's different from large language models. With large language models, we're thinking of chatbots that can talk about anything, and one of the things they can do is a kind of reasoning, but it's not as good as hand-building something for that specific task.

这挺有意思的，因为手工构建的东西最终非常僵化且脆弱。但与此同时，生成式 AI 方法的灵活性又太松散。你希望其中有些刚性。

It's kind of interesting because hand-building something ends up very rigid and brittle. But at the same time, the flexibility from the generative AI approach is too floppy. You want the rigidity in there.

嗯，也许吧，也许不是。我认为人类事务的许多例子并非如此非黑即白。

Well, maybe or maybe not. I think many examples of human affairs are just not as black and white as that.

你知道，也许你希望事情更模糊一些，即使是在简单的日常事务中，比如，花园的这个角落放什么花好呢？嗯，那个角落已经有了一些玫瑰，那些玫瑰是黄色的，所以我们不能有太多黄色，也许我们需要把它们移到花园的另一个角落。但同时，这是真正的推理，还是 AI 只是在模仿训练数据中存在的结构良好的论点，只不过是在一个新颖的环境中？

And you know, you do maybe want things to be a bit more blurry even in sort of simple everyday things like, you know, what would be good flowers to put over in this corner of the garden? Well, you know, we've already got some roses in that corner there, and those roses are yellow, so we'd have but we can't have too much yellow, so we maybe we'd need to move them to the other corner of the garden. But then at the same time though, is this real reasoning, or is this just the AI kind of mimicking well-structured arguments that have existed in the training data, but just in a sort of novel environment?

是的。当然，这引出了一个问题：什么是真正的推理？我认为真正的推理是什么并没有写在天空中。这取决于我们来定义真正的推理或推理的概念。所以我们有，你知道，我们之前讨论过那种逻辑学家所做的数学推理，过去由定理证明器完成的那种，今天也是如此。但那是，你知道，当人们最初使用推理这样的术语时，他们并不是在考虑那种事情。当我们在日常生活中使用推理这个词时，我们也不是在考虑那种事情。所以如果你和一个大型语言模型聊天，关于你的花园，你有点说，我在考虑种什么植物，它说，嗯，也许你应该在这种位置考虑这种植物，因为这对土壤最好，而且考虑到你说过那里风很大，你知道，我们就会说它在提供理由。我的意思是，它在提供理由，至于这些理由从何而来是另一回事。所以人们可能会说，它只是在模仿训练集中的内容，但你知道，它可能从未见过完全相同的例子。所以它在某种程度上超越了训练集。我认为它只是用日常的推理概念以日常的方式使用，所以称之为推理。

Yeah. Well, of course, that begs the question, what is real reasoning? I don't think there's it's not written in the sky, you know, what real reasoning is. It's up to us to define the concept of real reasoning or of reasoning. And so we have that, you know, we were talking earlier on about kind of mathematical reasoning of the sort that logicians do and that was done by kind of theorem provers in the past and so on and today. But that's you know that's when people were first using the terms like reasoning they weren't thinking of that kind of thing. And when we use the word reasoning in everyday life, we're not thinking about that sort of thing. So if you're chatting away to a large language model and about your garden and you sort of say, I'm thinking about what plants are and it says, well, you know, maybe you should consider this kind of plant in that kind of location because that's best for the soil and given you said that the winds, you know, it's windy there and you know, we would just say that that is supplying reasons. I mean, it is supplying reasons for now where they come from is another matter. So people might say, well, it's just mimicking what's in the training set, but you know, it's probably never seen exactly that example exactly before. So it's moving beyond the training set to a certain extent. And I think it's just using the everyday concept of reasoning in an everyday way to call that reasoning.

我回想起早期哲学家希望人工智能拥有的不同特征，推理是其中之一。但还有图灵测试，当然一直被提起，作为一种测试人工智能能力的方式。我的意思是它有点争议，对吧？我想就它作为 AI 能力测试的效果而言，你怎么看？你认为它曾经是一个好的测试吗？

I'm just thinking back to some of the different characteristics that the earlier philosophers wanted artificial intelligence to have and reasoning being one of them. But then also the Turing test which of course gets brought up all the time about a way to test for the capability of an artificial intelligence. I mean it's kind of controversial, right? I suppose in terms of how good it ever would have been as a test for the capability of AI. How what was your take on it? Do you think it was ever a good test?

不，我一直认为这是一个糟糕的测试，但却是引发哲学讨论的绝佳催化剂。再次，有点后见之明，我可能会稍微修正我的一些观点，因为我曾经非常坚信具身是智能的一个关键方面，是实现智能的关键，而这与图灵测试完全无关。是的，图灵测试明确与具身无关，因为图灵测试中，提醒大家它是什么。你有两个主体，一个是人类，另一个是计算机，然后有一个裁判。人类裁判看不到哪个是计算机哪个是人类，他们只能通过一种聊天式的界面与这些主体交流。他们看不到它们是否具身。所以我们可以很容易地假设计算机可能是今天的大型语言模型，在这种情况下，我不得不说今天它们几乎可以通过图灵测试。我的意思是，我们已经到了那个地步，这真的很惊人。但我曾经认为这是一个糟糕的测试，因为它没有测试任何这些具身技能，所以你真的需要一个机器人来测试某物是否具备我们日常认知的能力，比如泡茶之类的，否则它只是一种非常狭窄的智能形式。是的，它完全与语言和推理有关，而与进化在语言之前赋予我们和其他动物的那些能力无关，对吧？即操纵、移动、导航和利用日常物理世界的能力。

No, I've always thought it was a terrible test, but a really great spur to philosophical discussion about things. And again, with a bit of hindsight, maybe I might backtrack a little on a few of my views because I was certainly very much of the opinion that embodiment was a critical facet of intelligence, critical for achieving intelligence which doesn't come anywhere near the Turing test at all. Right. No, the Turing test is absolutely explicitly nothing to do with embodiment because in the Turing test, just to remind people what it is. So you have two subjects as it were. One is a human and the other is the computer and then you have a judge. The human judge can't see which is the computer and which is the human and they're only talking to these subjects through a kind of chat-like interface. They can't see whether they're embodied or not. So we can easily suppose that the computer might be one of today's large language models in which case I have to say that today they would pretty much pass the Turing test. I mean we've got to that point which is amazing really. But so I used to think that it was a bad test because it didn't test any of these embodied skills so you'd need a robot really to test whether something was capable of the kind of everyday cognition that we all put to use when we're for example making a cup of tea or something because otherwise it's a very narrow form of intelligence. Yes, it's all to do with language and reasoning and not to do with the kinds of things that evolution developed in us and in other animals before language, right? Which is the ability to manipulate and move around with and navigate and exploit, in the best sense of the word, the everyday physical world.

所以，实际上，这真的很有趣。这太有趣了，因为我经常想，也许我们目前的大型语言模型可以通过图灵测试，但如果你朝电脑扔一个球，它们不会退缩。哦，确实不会。从某种意义上说，这些是更深层次的形式。也许我们不会把它们归类为我们所说的智能，但最终它确实是一种智能形式。

So, actually, that's really interesting. That's so interesting because I often think about how fine maybe the large language models we have at the moment can pass the Turing test but they don't flinch if you throw a ball at your computer. Oh no indeed. And in a sense there are these sort of as you say much deeper forms. Maybe we wouldn't class them as intelligence in the way that we talk about it but ultimately it sort of is a form of intelligence.

我认为它确实是一种智能形式。此外，我认为在生物案例中，现在我必须加上一个限定，在生物案例中，我们思考、推理和说话的能力非常植根于我们与日常世界的互动。如果你想想，你几乎所有的日常语言都在使用空间隐喻。我的意思是，它们完全渗透了我们的日常语言。甚至“渗透”这个词本身。是的，绝对如此。一切都是植根于的。我用了“植根于”，你知道。所以我们一直使用这些东西，因为我们从根本上来说是物理存在。因为我们从根本上来说是物理存在，因为我们的大脑进化是为了帮助我们在物理世界中生存和繁衍。是的。同时与所有其他做着同样事情的存在互动，对吧？

I think it very much is a form of intelligence. Moreover, I think that in the biological case, and now I have to caveat all these things by saying in the biological case, our ability to think and to reason and to talk is very much grounded in our interaction with the everyday world. If you think about almost all of your everyday speech is using spatial metaphors. I mean, they completely permeate our everyday speech. Even the word permeate. Permeate. Yeah, absolutely. All grounded. I use the grounded, you know. So we just use those kinds of things all the time because we're fundamentally physical beings. Because we're fundamentally physical beings and because our brains have evolved to help us to survive and reproduce in this physical world. Yeah. And while interacting with all these other beings that are doing the same thing, right?

因为当你想测试人工智能的能力时，有一些替代方案。请给我讲讲我们有哪些潜在的替代方案。

Because there are some alternatives when you are trying to test for the capability of an artificial intelligence. Just talk me through some of the potential alternatives that we have.

嗯，我想你可能想到了加兰测试，我称之为加兰测试，这要追溯到电影《机械姬》，由亚历克斯·加兰执导。剧本中有一段，亿万富翁内森在和迦勒谈话，迦勒是被带来与机器人艾娃互动的人，迦勒说：“哦，我是来对艾娃进行图灵测试的。”内森说：“哦，不。我们早就过了那个阶段。艾娃能轻松通过图灵测试。”关键是让你知道她是机器人，然后看你是否仍然认为她有意识。哇。这就是我所说的加兰测试。它在两个方面不同于图灵测试。

Well, I think perhaps you've got in mind the Garland test, what I call the Garland test, which goes back to the film Ex Machina, which was directed by Alex Garland, of course. And there's a bit in the script where Nathan, the billionaire guy, is talking to Caleb, and Caleb, who's the guy who's been brought in to interact with Ava, the robot, and Caleb says, "Oh, I'm here to kind of conduct a Turing test on Ava." And Nathan says, "Oh, no. We're way past that. Ava could pass the Turing test easily." The point is to show you she's a robot and see if you still think she's conscious. Wow. And that's what I call the Garland test. And it's different from the Turing test in two respects.

首先，那个裁判，也就是 Caleb，能看到她是个机器人。但在图灵测试中，裁判看不到哪个是哪个。这里的想法是，Caleb 知道她的大脑是 AI 大脑，却仍然把这些特征归因于她。而且这个特征也不同，不是智能——不是‘她能思考吗？’而是‘她有意识吗？’这完全是另一种测试。我认为智能和意识是不同的东西，我们可以把它们分开，解耦。所以当我第一次读到电影剧本，看到 Caleb 和 Nathan 的那些台词时，我在我的版本旁边写下了‘完全正确’加一个感叹号，因为我觉得 Alex 完全抓住了那里一个非常重要的想法。在我的写作中，我称之为 Garland 测试，不少人也注意到了，并称之为 Garland 测试。

So, first of all, the sort of judge, as it were, which in that case is Caleb, can see that she's a robot. But in the Turing test, the judge can't see which is which. Here, the idea is that Caleb knows that her brain is an AI brain, yet still attributes these characteristics to her. And the characteristic in question is different because it's not intelligence—it's not 'can she think?' but 'is she conscious?' which is an entirely different test. I think intelligence and consciousness are different things, and we can disentangle them, dissociate them. So when I first read the script of the film and those particular lines were in there for Caleb and Nathan, I wrote next to it in my version 'spot on' with an exclamation mark because I thought Alex totally nailed a really important idea there. In my writing, I call this the Garland test, and quite a few people have picked up on that and called it the Garland test as well.

有没有一个测试，如果 AI 能通过，会让你印象深刻？

Is there a test that would really impress you if an AI were able of passing it?

我一直对 François Chollet 的 ARC 测试印象深刻。ARC 代表抽象推理语料库。这些是你在智商测试中看到的那种小图像序列。图像成对排列。第一张图像有点像素化，有单元格和你可以解释为物体或线条的小东西。挑战是找出从第一张图像到第二张图像的规则。然后你必须将该规则应用于第三张图像。首先，他保留并完全保密了所有测试图像，所以你无法通过知道实际测试版本或将其用于训练集来作弊。此外，他非常精心地设计它们，使得每个规则与其他规则完全不同，你通常需要找到某种直观的日常常识知识的应用——把某物看作朝这个方向移动的液体，或者想象某物在生长等等。所以它在某种程度上需要接地。但最近，人们已经能够以更蛮力的方式在这些测试上取得显著进展。所以我觉得这些解决方案并没有真正抓住原始测试的精神。

I always was very impressed with François Chollet's ARC tests. ARC stands for Abstract Reasoning Corpus. These are little sequences of images of the sort you get in IQ tests. The images are arranged in pairs. You have the first image, it's kind of pixelated, with cells and little things you can interpret as objects or lines. The challenge is to work out a rule that takes you from one image to the second one. Then you've got to apply that rule to a third image. First of all, he held out and made completely secret all of the test ones, so you couldn't game it by knowing what the actual test versions were or using them in a training set. Also, he very carefully designed them so that each rule was completely different from the others, and you usually had to find some kind of intuitive application of everyday common sense knowledge—seeing this as a liquid moving in this direction, or imagining this thing growing or something. So it required grounding in a way. But recently, people have been able to make significant progress on these in a more brute force kind of way. So I feel that the solutions are not really getting at the spirit of the original test quite so much.

没错。我想在某种程度上，一旦你设定了一个指标，一旦你设定了一个门槛，说‘一旦我们跨过这个门槛，我们就拥有了能力、智能、意识等等’，它就会改变测试本身的整个性质。人们会开始钻测试的空子，对吧？这就是古德哈特定律。

Well, that's it. I guess in a way, as soon as you set a metric, as soon as you set a bar for 'once we've crossed this threshold, then we will have capability, intelligence, consciousness, whatever it might be,' it sort of changes the whole nature of the test itself. People are going to start gaming the test, right? It's Goodhart's law.

完全正确。

Absolutely.

很多来这个播客的嘉宾都表示需要警惕将这些事物拟人化。你是那种认为我们不应该这样做的人吗？

A lot of people who come on this podcast have expressed real need for caution about anthropomorphizing these things. Are you one of those people who thinks that we shouldn't?

嗯，我认为有不同的看待方式，而且我认为拟人化有好有坏。一方面，人们可能会开始与 AI 系统建立关系——友谊、陪伴、指导——如果被误导认为他们可以信任与之交谈的对象，或者真的爱上了它，或者它真的关心他们，那可能是一件坏事。在另一个极端，如果 AI 系统只是使用‘我’这个词，那么我认为这是一种相当无害的自我拟人化形式。我们甚至看到公交车侧面写着‘我暂停服务’，我们对此没有意见。所以我不认为我们应该对大型语言模型也有意见。但我认为我们确实倾向于拟人化事物。当汽车里有独立的卫星导航时，我经常把它拟人化。我常常想，‘哦，你这蠢东西，它以为我们在做这个。’我认为这是人类的天性。

Well, I think there are different ways of looking at this, and I think there are good and bad forms of anthropomorphization. On the one hand, people can start to form relationships as they see it with AI systems—friendships, companionships, mentorships—and that can potentially be a bad thing if they are misled into thinking that they can trust the thing they're talking to, or that they're really in love with it, or that it really cares about them. On the other end of the spectrum, if an AI system is just using the word 'I', then I think that's a pretty harmless form of self-anthropomorphization. We even see buses that say things like 'I am out of service' and we don't have a problem with that. So I don't see why we should have a problem with that with large language models either. But I think we do tend to anthropomorphize things. When we had satnavs in cars that weren't just in our phones, I used to anthropomorphize the satnav all the time. I used to think, 'Oh, you stupid thing, it thinks we're doing this.' It's a natural human tendency, I think.

那我们使用的其他词呢？你举的例子，卫星导航说‘哦，它以为我们在停车场’或者‘哦，它相信这是……它搞错了。它误解了。’这些都是非常以人为中心的词，不是吗？

What about the other words that we use? I mean the example that you gave of the satnav saying 'oh it thinks we're in the car park' or 'oh it believes that this is... it got this wrong. It misunderstood this.' Those are all very human-centric words, aren't they?

是的，完全正确。所以这些是哲学家常说的民间心理学的例子。我们有这种民间心理学，使用像信念、欲望和意图这样的词，不仅适用于其他人类和其他动物，也适用于物体。这就是哲学家丹·丹尼特所说的采取意向立场。如果我们谈论某物并认为它基于信念和目标行事，并据此做出理性决策，我们就对它采取了意向立场。这是一种非常有用的思考方式，适用于许多事物，比如我们的卫星导航或国际象棋计算机。对丹·丹尼特来说，这是他用的一个例子：一个国际象棋计算机——‘哦，它想把皇后向前走，因为它认为我会用车来防守这一排。’这充满了关于信念和目标的意向性民间心理学语言。

Yeah, absolutely. So those are examples of what philosophers often call folk psychology. We have this folk psychology where we use words like belief, concepts like belief, desire, and intention, which we can apply not just to other humans and other animals, but to objects as well. It's what the philosopher Dan Dennett called taking the intentional stance. We adopt the intentional stance towards something if we talk about it and think about it as if it acted on the basis of having beliefs and goals and carrying out rational decisions on the basis of those things. That's a very useful way of thinking about many things, such as our satnav or a chess computer. For Dan Dennett, that was one of the examples he used: a chess computer—'oh, it wants to get the queen forward because it thinks I'm going to use my rook to defend this rank.' That's full of this intentional folk psychological language about beliefs and goals.

那有问题吗？如果我们开始对 AI 使用信念、意图和欲望这些概念？

Is that problematic? If we start using that idea of beliefs and intentions and desires about the AI?

只有当我们开始以误导我们认为事物拥有它们实际上并不具备的能力的方式使用这些概念时，才会有问题。所以我认为这就是问题所在。

It's only problematic if we start to use these things in ways that mislead us into thinking that things have capabilities that they don't really have. So I think that's where it becomes problematic.

《大英百科全书》的实体卷册不知道阿根廷赢了世界杯，因为它太旧了。所以如果你说那句话，完全合理。但如果有人说，你怎么不和它聊聊英格兰的足球实力，那就荒谬了。现在，有了大型语言模型，你可以和它们对话，这把我们可能说‘它其实不懂 X、Y、Z’的边界又推远了一点。我在想，这是否有更深层的东西，关于人类需要或渴望 AI 拥有这些特征，被拟人化。

The Encyclopedia Britannica, the physical volume, doesn't know that Argentina won the World Cup because it's too old. So if you made that remark, it would make perfect sense. But if someone said, why don't you have a conversation with it about England's football prowess, that would be ridiculous. Now, with large language models, you can have a conversation with them, and it pushes the boundary of where we might start to say it doesn't really X, Y, Z. I wonder if there's something deeper about this human need or desire to want AI to have these characteristics, to be anthropomorphized.

这是个很有趣的问题。这又回到了语言。我们倾向于拟人化事物，因为它们非常擅长使用语言。对我们来说，擅长使用语言的只有其他人类。所以突然进入一个机器能说话的世界，这非常奇怪。这令人震惊。

That's a really interesting question. It comes back to language. We're inclined to anthropomorphize things because they're really good at using language. For us, the only things that are good at using language are other humans. So it's very strange to suddenly be in a world where machines can talk. That's astonishing.

确实令人震惊。今天出生的每个孩子都将在一个从未知道机器不能和他们说话的世界中长大。这难道不非凡吗？

It is astonishing. Every child born today will grow up in a world where they've never known a world in which machines can't talk to them. Isn't that extraordinary?

确实如此。这对我们所有人的影响很难说。

It really is. And what the implications are for us all is hard to say.

想到人类如何扎根于物理世界，AI 的具身方面远远落后于语言。你认为一旦我们有了好的、有效的具身 AI，智能会有大的飞跃吗？

Thinking about how grounded humans are in the physical world, the embodied aspect of AI has lagged behind language. Do you think we'll see a big upstep in intelligence once we get good and effective embodied AI?

这可能会带来很大不同。当前的大型语言模型很难看出极限在哪里。有时你会觉得 AI 并没有真正理解某件事，只是在假装。那种在深层常识层面上真正理解事物的通用能力可能仍然需要具身。它需要涉及与真实物理世界及其空间组织互动的训练数据。这其中有某种根本性的东西。

It might make a big difference. Current large language models are hard to discern where the limits are. Sometimes you get the impression that the AI doesn't really grok something, doesn't deeply understand, and it's been faking it. That general ability to really get things on a deep, common sense level may still require embodiment. It requires training data involving interacting with a real world of physical objects with spatial organization. There's something fundamental about that.

如果理解可以从更多数据中涌现，那么意识呢？我们能期待它发生，还是已经发生了？

If understanding can emerge from more data, what about consciousness? Can we expect it to happen or has it already happened?

首先，我们可以将智能或认知与意识分离开来。我们可以想象非常能干的事物，但我们不想赋予其意识。但赋予意识意味着什么？这个概念可以分解成许多部分。例如，对世界的意识：大型语言模型在这方面并不具有世界意识。自我意识包括对我们自己身体和意识流的意识。还有元认知，以及情感方面或感知能力——感受和受苦的能力。在人类身上，这些是捆绑在一起的。但我们可以将它们分开。例如，一个机器人吸尘器有某种世界意识，但我不会称之为意识，因为那会带来其他方面。你可以将意识分解成这些不同的方面。

First, we can dissociate intelligence or cognition from consciousness. We can imagine very capable things that we don't want to ascribe consciousness to. But what does it mean to ascribe consciousness? The concept can be broken down into many parts. For example, awareness of the world: large language models are not aware of the world in that respect. Self-awareness includes awareness of our own body and our stream of consciousness. There's also metacognition, and the emotional side or sentience—the capacity to feel and suffer. In humans, these all come as a bundle. But we can separate them. For instance, a robot vacuum cleaner has a kind of awareness of the world, but I wouldn't call it consciousness because that brings in other aspects. You can break down consciousness into these different aspects.

所以那里有一种对世界的意识。我不认为有任何自我意识。当然没有感受痛苦的能力。因此，在一个大型语言模型中，可能没有那种感知意义上的世界意识，但也许有某种自我意识或反思能力，反思性的认知能力。例如，它们可以谈论对话中早些时候谈论过的事情，并且能够以反思的方式做到这一点，这有点像我们拥有的自我意识的某些方面。我认为将它们视为有情感是不合适的。它们无法体验痛苦，因为它们没有身体。我认为基本上我们可以分解这个概念。那么问题'人工智能能否有意识？'就好像它是一个二元的东西——从一开始就是错误的问题。我确实认为这是个错误的问题，而且我认为它在很多方面都是错误的。所以，刚才我们谈到它实际上是一个多方面的概念，但我也认为我们倾向于对意识持有非常深刻的形而上学承诺，将其视为某种神奇的东西，一种形而上学的东西。因此，某物是否有意识的问题不是共识问题，也不仅仅是我们的语言问题，而是存在于形而上学现实或上帝心中或柏拉图天堂之类的东西。但最终我确实认为那是思考意识的错误方式。

So there's a kind of awareness of the world there. I don't think there's any self-awareness. There's certainly no capacity for suffering. And so in a large language model, there might not be awareness of the world in that perceptual sense, but maybe there's some kind of self-awareness or reflexive capabilities, reflexive cognitive capabilities. They can talk about the things that they've talked about earlier in the conversation, for example, and can do so in a reflective manner, which kind of feels a little bit like some aspects of self-awareness that we have. I don't think that it's appropriate to think of them in terms of having feelings. They can't experience pain because they don't have a body. I think we can take the concept apart basically. So then the question 'Can AI be conscious or not?' as though it's a binary thing—it's the wrong question from the off. I do think that is the wrong question, and I think it's wrong in many ways. So, just then we were talking about the fact that it's actually a sort of multifaceted concept, but also I think that we tend to have these very deep metaphysical commitments to the idea of consciousness as some sort of magical thing that is a metaphysical thing. So the question of whether something is conscious or not is not a matter of consensus or a matter of just our language, but it's something that is out there in metaphysical reality or in the mind of God or in the platonic heaven or something like that. But ultimately I do think that that's the wrong way of thinking about consciousness.

那么让我们谈谈你描述的意识的一个方面，即情感方面，一种感受痛苦的能力，不一定是身体上的痛苦，还有情感上的痛苦，以及某种情感上的自我意识。你认为这是否会作为智能的自然结果而出现，即如果你构建足够智能的东西，在某个时刻这就会发生？还是说生物体以及我们经历的进化过程有某种独特之处，导致这种能力无法在机器中复制？

Let's take one aspect of consciousness then that you described about the sort of emotional side, an ability to suffer but not necessarily physical pain, emotional pain too, and sort of a sense of self in the emotional way. Do you think this is something that will just emerge as a natural consequence of intelligence, that if you build something that is intelligent enough, at some point this is going to happen? Or is there something unique about biological creatures and the process of evolution that we've been through that has resulted in that that can't be replicated in a machine?

我认为你的问题没有对错之分。你知道，我认为我们只能拭目以待，看看我们创造出什么样的东西，以及我们最终如何对待它们、谈论它们和思考它们。我认为直到它们真的出现在我们中间，我们才能真正知道。然后我们就会被引导以特定的方式思考它们、谈论它们和对待它们。在这方面，我喜欢举的例子是章鱼。章鱼最近被纳入英国立法，被归入我们需要关心其福利的类别。我认为这是许多事情共同作用的结果。现在公众接触章鱼的机会多了很多。你不需要真的潜入水中与章鱼互动，就能知道与它们相处是什么感觉，因为有各种精彩的纪录片和书籍，比如彼得·戈弗雷-史密斯写的那些关于与章鱼互动的书。这些叙述和纪录片让我们感受到与章鱼相处、与章鱼相遇是什么感觉。然后你几乎无法不把它视为一个有意识的同类生物。但与此同时，科学进步也在补充这一点。科学家们研究章鱼的神经系统，意识到它们的神经系统与我们的相似程度，以及当我们经历痛苦时，可以在它们的神经系统中找到与我们类似的方面。所以综合考虑这些因素，我认为这会影响我们思考它们、谈论它们和对待它们的方式。所以我认为同样的事情也会发生在人工智能系统上。我是否认为我们可能被误导这个问题有对错之分？我认为这是一个非常深刻且困难的形而上学哲学问题。

I don't think there is a right or wrong answer to your question there. You know, I think we just have to wait and see what things we bring into the world and how we end up treating them and talking about them and thinking about them. And I don't think we really know until they're among us, as it were, you know, these things that we're building. Then we will just be led to think about them and talk about them and treat them in a particular way. So an example I like to think of in this regard is the octopus. Octopuses have recently been brought into UK legislation, brought into the category of things that we have to care about the welfare of. That's as a result of lots of things, I think, happening. So, the public has been exposed to being with octopuses a lot more now. And you don't have to literally be under the water and poking around with octopuses to know what it's like to be with them because there are all kinds of wonderful documentaries and wonderful books by like Peter Godfrey-Smith, who has these great books about interacting with octopuses and so on. And those sorts of narratives and documentaries give us a feel for what it's like to be with an octopus, what it's like to have an encounter with an octopus. And then you sort of can't help yourself but to see it as a fellow conscious creature. But complementing that is the scientific progress as well. At the same time, scientists study the nervous systems of octopuses and realize the extent to which their nervous systems are similar to ours and the way that when we experience pain, you can find analogous aspects of their nervous systems to ours. So taking all these things together, I think that tends to affect the way we think about them and the way we talk about them and the way we treat them. So I think the same kind of thing will happen with AI systems. Do I think there's a right or wrong answer to whether we could be misled there? I think that's a really deep and difficult metaphysical philosophical question.

不过我确实在想，关于痛苦这一点在我看来与其他方面不同，因为元认知、世界意识等不一定有这些伦理含义。但关于痛苦，你不会希望你的鞋子有意识，你不会希望一辆叉车有意识，除非它们碰巧真的很喜欢当叉车。所以我们对这个特定方面是否要更加小心一点？如果有可能创造出真正能够感受痛苦的东西，那么我们应该非常认真地思考是否应该这样做。我倾向于认为我们目前拥有的任何东西都不是这种情况。但有些人会反驳这一点，如果我们以大型语言模型为例。好吧，在某种程度上，它们所做的是下一个词预测。但为了能够像现在这样做得非常好，它们必须学习并获得各种涌现机制。所以谁知道在语言模型中那庞大得惊人的数千亿权重中是否已经学到了某种涌现机制，例如，是否学到了某种具有真正理解能力的东西，无论那意味着什么，甚至意识。

I do wonder though, I mean that point about suffering to me seems different to the others because metacognition, the sense of the world, etc., there's not these ethical implications necessarily about those. But with suffering, like you wouldn't want your shoes to be conscious, you wouldn't want a forklift truck to be conscious unless they happen to really like being a forklift truck. So do we have to be a tiny bit more careful about that particular aspect of it? If there were the prospect of being something that is genuinely capable of suffering, then we should think very hard about whether we should do it or not. I tend to think that that's not the case with anything that we've got at the moment. But some people will push back against that if we take the example of large language models. Well okay, so there's one level in which what they do is next-token prediction, next-word prediction. But in order to be able to do that really really well in the way that they can at the moment, they've had to learn and acquire all kinds of emergent mechanisms. So who knows whether or not there's some kind of emergent mechanism that has been learned in the weights of this enormous staggeringly huge number, hundreds of billions of weights in a language model, whether some mechanism hasn't been learned there that has, for example, genuine understanding in it, whatever that means, or even consciousness.

再次回到具身性，我一直认为，只有在能够与某物共享世界、并像与章鱼、狗、马等动物那样相遇的背景下，谈论意识才是真正合理的。与那种动物共同存在于世界中，一起对事物做出反应，那么我毫不怀疑它们是有意识的。这对我来说是一个基本案例。现在，对于大型语言模型，你无法以那种方式与它们处于同一个世界，你无法与它们一起闲逛并与物理对象互动。对于今天的大型语言模型，对吧？所以，在我看来，在这种背景下使用意识的语言，正如维特根斯坦所说：这是让语言放假。它被使用得远远超出了其正常用途。

Coming back to embodiment again, I've always been of the view that it's only really legitimate to talk about consciousness in the context of something we can share a world with and have that kind of encounter that we have with an octopus or a dog or a horse or whatever. Being together in the world with that animal and responding to things together, then I'm in no doubt that they are conscious. That's a kind of primal case for me. Now, with a large language model, you can't be in the same world as them in that kind of way, and you can't hang out with them and interact with physical objects. With today's large language models, right? So, to my mind, using the language of consciousness in that context is what Wittgenstein would say: it's taking language on holiday. It's using it so far outside of its normal use.

你知道，也许这不太恰当，但这种情况可能会改变。而且我与大语言模型互动得越多，与它们进行越复杂有趣的对话，我就越倾向于认为，也许我想扩展意识的语言，弯曲它、改变它、扭曲它、创造一些新词、以各种方式打破它，以适应我一直在互动的这些新事物。我知道你花了很多时间与大语言模型互动。我甚至看到有人称你为著名的提示词低语者。你的秘诀是什么？

You know, maybe it's inappropriate, but that can change. And the more I interact with large language models, the more I have these sophisticated and interesting conversations with them, the more I'm inclined to think, well, maybe I want to extend the language of consciousness, bend it, change it, distort it, make up some new words, break it apart in ways that are going to fit these new things that I'm interacting with all the time. I know you've spent a lot of time interacting with these large language models. I've actually seen you described as a renowned prompt whisperer. What's your secret?

嗯，一个秘诀就是把大语言模型当作人类来对话。所以，如果你认为它们是在扮演一个人类角色，比如一个非常聪明且乐于助人的实习生，那么你就应该像对待一个聪明且乐于助人的实习生那样对待它们，并像跟一个聪明且乐于助人的实习生那样跟它们说话。例如，保持礼貌，说‘清楚了吗？’、‘请’和‘谢谢’。根据我的经验，这样做你会得到更好的回应。

Well, one secret is to talk to the large language model as if it were human. So, if you think that what they're doing is roleplaying a human character, such as a very smart and helpful intern, then you should treat them like a smart and helpful intern and talk to them as if they were a smart and helpful intern. For example, just being polite and saying, 'Is that clear?' and 'Please' and 'Thank you.' And in my experience, you get better responses out of things if you do things that way.

你会说请和谢谢吗？

Do you say please and thank you?

你可以说请和谢谢。是的。现在，有一个很好的科学理由解释为什么这样做可能会获得更好的性能。因为如果它在角色扮演，比如说扮演一个非常聪明的实习生，那么如果它没有被礼貌对待，它可能会扮演得有点暴躁。这只是在模仿人类在这种情况下会做的事情。所以这种模仿可能会延伸到，如果老板有点专横，它就会变得不那么积极响应。

You can say please and thank you. Yeah. Now, there's a good scientific reason why that might get better performance out of it. Because if it's roleplaying, say it's roleplaying a very smart intern, then it might roleplay being a bit more stroppy if it's not being treated politely. It's just mimicking what humans would do in that scenario. So the mimicry might extend to being a bit less responsive if their boss is a bit bossy.

我太喜欢这个了。我想回到我们开始的地方，也就是关于我们如何看待 AI、我们用来描述它的语言以及我们如何在脑海中构建它。你认为我们需要一种新的方式来谈论 AI，既承认它的潜力而不高估它，但同样也不轻视它能做的事情吗？

I absolutely love that. I think I want to return to where we started, which is about how we think about AI and the language we use to describe it and how we frame it in our minds. Do you think that we need a new way of talking about AI that both acknowledges its potential without overestimating it, but similarly isn't dismissive of the things that it can do?

我认为这正是我们需要的。在我的一篇论文中，我用了‘异类心智类实体’这个短语来描述大语言模型。所以我认为它们在某种程度上是异类心智类实体。它们有点像心智，而且越来越像心智。这里使用‘类’这个字有一个非常重要的原因，因为我想在它们是否真正有资格成为心智这个问题上保持谨慎。所以我可以通过使用‘心智类’来回避这个问题。它们是异类的，因为它们在语言使用上不像我们，但其他方面，首先它们是离身的。也许有一些非常奇怪的自我概念适用于它们，但它们也是相当异类的实体。所以我认为它们是异类心智类实体，而我们还没有合适的概念框架和词汇来谈论这些异类心智类实体。我们正在努力，而且它们在我们身边越多，我们就越会发展出新的方式来谈论和思考它们。

I think that's exactly what we need. In one of my papers, I use the phrase 'exotic mindlike entities' to describe large language models. So I think that they are to a degree exotic mindlike entities. They are kind of mindlike and they're increasingly mindlike. Now there's a very important reason for using the little hyphen 'like' there, which is because I want to hedge my bets as to whether they really qualify as minds. And so I can wriggle out of that problem by just using 'mindlike'. They're exotic because they're not like us in language use, but in other respects they're disembodied for a start. There are really weird conceptions of selfhood that are applicable to them maybe, but they are quite exotic entities as well. So I think of them as exotic mindlike entities and we just don't have the right kind of conceptual framework and vocabulary for talking about these exotic mindlike entities yet. We're working on it, and the more they are around us, the more we'll develop new kinds of ways of talking and thinking about them.

不过有趣的是，你仍然倾向于那种类似生物的方法，而不是一个东西的概念。

It is interesting though that you are still going for the sort of the creature-like approach rather than the idea of a thing.

嗯，你知道，‘实体’是一个相当中性的术语，不是吗？我想你也可以说‘东西’，‘异类心智类东西’，如果你更喜欢的话。

Well, you know, an entity is a pretty neutral term, isn't it? I suppose you could just say 'thing', 'exotic mindlike thing' if you prefer.

是的，我们就用这个吧。我想我们为新的名词推广这个。

Yeah, let's go with that. I think let's push for that for the new N.

好吧。但我的意思是，我不能，Hannah，因为我现在已经在很多出版物中用了‘实体’这个词。所以是‘异类心智类实体’。

Okay. But I mean, I can't, Hannah, because I've used the word 'entity' in that context in many publications now. So 'exotic mindlike entities'.

我喜欢。我非常喜欢。非常感谢你加入我们。很愉快，Hannah。谢谢。

I like it. I like it a lot. Thank you so much for joining us. It's been a pleasure, Hannah. Thank you.

做这个播客几年来的好处之一就是，你真的能看到 AI 前沿的人们，他们的观点如何随着时间变化。过去几年在很多方面都真正改变了游戏规则。关于智能在多大程度上需要物理身体。关于我们需要在多大程度上扩展意识定义，以解释这些心智类实体在未来几年可能以微妙不同的方式运作。谁知道呢？但如果过去的预测有任何指示，我们对明天的科学和技术唯一知道的就是，它将与我们今天想象的截然不同。你收听的是 Google DeepMind 播客，我是 Hannah Fry 教授。如果你喜欢这一集，请订阅我们的 YouTube 频道。你也可以在你最喜欢的播客平台上找到我们。当然，我们还有更多关于各种主题的剧集即将推出。所以，请查看它们。下次见。

One of the nice things about having done this podcast for a number of years is that you really get to see how the people at the frontier of AI, how their opinions change and shift over time. And the last few years have been a real game changer in all sorts of ways. About the extent to which intelligence requires a physical body. About how much we need to expand our definition of consciousness to account for the subtly different ways that these mindlike entities can operate in the next few years. Well, who knows? But if past predictions are any indication, the only thing we know about tomorrow's science and technology is that it will be radically different to what we imagine today. You have been listening to Google DeepMind the podcast with me, Professor Hannah Fry. If you enjoyed this episode, then do subscribe to our YouTube channel. You can also find us on your favorite podcast platform. And of course, we have plenty more episodes on a whole range of topics to come. So, do check those out. See you next time.